Task: Write a program in Python to detect the change from the images (Pre.jpg and Post.jpg) of the same map location.¶

Solution!👇¶

Method 1¶

Finding Image Difference using OpenCV and Python.¶

In [1]:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

/kaggle/input/images/Pre.jpg
/kaggle/input/images/Post.jpg
In [3]:
# importing the necessary packages
from skimage.metrics import structural_similarity
import imutils
import cv2
In [4]:
# load the two input images
imageA = cv2.imread('/kaggle/input/images/Pre.jpg')
imageB = cv2.imread('/kaggle/input/images/Post.jpg')
# convert the images to grayscale
grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)
In [5]:
# Plotting the Input Images.
plt.figure(figsize=(15,10))
plt.suptitle('Input Pre.jpg Image!', fontsize=14, fontweight='bold')
plt.title("Pre.jpg")
plt.imshow(imageA)
Out[5]:
<matplotlib.image.AxesImage at 0x7f72d4918a50>
In [6]:
# Plotting the Input Images.
plt.figure(figsize=(15,10))
plt.suptitle('Input Post.jpg Image!', fontsize=14, fontweight='bold')
plt.title("Post.jpg")
plt.imshow(imageB)
Out[6]:
<matplotlib.image.AxesImage at 0x7f72c7d8ca90>
In [ ]:
# compute the Structural Similarity Index (SSIM) between the two
# images, ensuring that the difference image is returned
(score, diff) = structural_similarity(grayA, grayB, full=True)
diff = (diff * 255).astype("uint8")
print("Similarity: {}".format(score))

# Threshold the diff image, and find contours which will showcase the regions in the images
# that are different
thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)

# loop over the contours and create bounding boxes on our two images
for c in cnts:
    # compute the bounding box of the contour and then draw the
    # bounding box on both input images to represent where the two images differ
    (x, y, w, h) = cv2.boundingRect(c)
    cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 255, 0), 2)
    cv2.rectangle(imageB, (x, y), (x + w, y + h), (255, 0, 0 ), 2)
    
# showing the output images

# Use below code to display the output into the notebook only.
plt.figure(figsize=(15,10))
plt.title("Pre Image"
In [7]:
plt.imshow(imageA)

Similarity: 0.3779238610196616
Out[7]:
'\n# Use this code to display the output into a separate window if you\'re using local machine.\ncv2.imshow("Original", imageA)\ncv2.imshow("Modified", imageB)\ncv2.imshow("Diff", diff)\ncv2.imshow("Thresh", thresh)\ncv2.waitKey(0)\n'
In [8]:
plt.figure(figsize=(15,10))
plt.title("Post Image")
plt.imshow(imageB)
Out[8]:
<matplotlib.image.AxesImage at 0x7f72c5156dd0>
In [9]:
plt.figure(figsize=(15,10))
plt.suptitle('More darker the shade area is, the Higher image difference is there!', fontsize=14, fontweight='bold')
plt.title("Difference")
plt.imshow(diff)
Out[9]:
<matplotlib.image.AxesImage at 0x7f72c50beed0>
In [10]:
plt.figure(figsize=(15,10))
plt.suptitle('The yellowish part is Showing the difference!', fontsize=14, fontweight='bold')
plt.title("Thresh")
plt.imshow(thresh)
Out[10]:
<matplotlib.image.AxesImage at 0x7f72c503afd0>

Method 2¶

Finding Image Difference using ImageChops module of Python Imaging Library (PIL)¶

In [11]:
from PIL import Image, ImageChops

image1 = Image.open('/kaggle/input/images/Pre.jpg')
image2 = Image.open('/kaggle/input/images/Post.jpg')

# It Returns the absolute value of the pixel-by-pixel difference between the two Images.
diff = ImageChops.difference(image1, image2)

# Displaying the Result.
if diff.getbbox():
    image = diff
    
image.save("Difference.jpg")  # Saving the output file as Image.
plt.figure(figsize=(15,10))
plt.suptitle('More Lighter the shade area is, the Higher Image difference is there!', fontsize=14, fontweight='bold')
plt.title("Heatmap Difference between 2 Images!")
plt.imshow(image)
Out[11]:
<matplotlib.image.AxesImage at 0x7f72c4fad9d0>
In [12]:
# It Returns the absolute value of the pixel-by-pixel Multiplication between the two Images.
diff = ImageChops.multiply(image1, image2)

# Displaying the Result.
if diff.getbbox():
    image = diff
    
image.save("Multiply_Difference.jpg")  # Saving the output file as Image.
plt.figure(figsize=(15,10))
plt.suptitle('More Lighter the shade area is, the Higher Image Similarity is there!', fontsize=14, fontweight='bold')
plt.title("Difference between 2 Images!")
plt.imshow(image)
Out[12]:
<matplotlib.image.AxesImage at 0x7f72c4f28710>
In [13]:
# It Returns the absolute value of the pixel-by-pixel Subtraction between the two Images.
diff = ImageChops.subtract(image1, image2, scale=1.0, offset=0)

# Displaying the Result.
if diff.getbbox():
    image = diff
    
image.save("Subtraction_Difference.jpg")  # Saving the output file as Image.
plt.figure(figsize=(15,10))
plt.suptitle('More Darker the shade is, the Higher Image difference is there!', fontsize=14, fontweight='bold')
plt.title("Difference between 2 Images!")
plt.imshow(image)
Out[13]:
<matplotlib.image.AxesImage at 0x7f72c4e9a850>

End!¶

In [ ]: